Japanese Patent Application Publication Number 2004-354111 published on Dec. 16, 2004 discloses object inspection method and apparatus (hereinafter referred to as a “first prior art”). The apparatus has a measuring device (sensor, microphone, etc.), a transducer, a competitive learning neural network, a display device and so on, and operates in either a learning mode or an inspecting mode. In the learning mode, the apparatus utilizes a learning data set that is a collection of data. Each member of the set is obtained by that the measuring device obtains a measurement signal from a measuring object and then the transducer extracts a feature (i.e., feature data) from the signal. Each member of the set is also assigned by a user to any one of assumed categories. Subsequently, a clustering map is made by sequentially entering each member of the learning data set into the network. All elements constituting the map correspond one-on-one to all output neurons constituting an output layer of the network. In the inspecting mode, if the feature data extracted through the transducer is entered into the network, the network judges the category of the data to specify a position of the category on the map. The display device then shows the clustering map and the position of the category. Accordingly, the measurement result (i.e., the category of the feature data obtained from the measurement signal) can be recognized visually.
Japanese Patent Application Publication Number 2005-115569 published on Apr. 28, 2005 discloses signal discrimination apparatus and method (hereinafter referred to as a “second prior art”). The apparatus has a competitive learning neural network, a display device and so on in the same way as the first prior art, and further has a signal processor located between a measuring device and the network. The processor is formed of a filter and a transducer.
The filter is configured to operate in any mode of a pass-through mode, an envelope (curve) mode, an FIR (finite impulse response) mode, a Wavelet transformation mode and a cepstrum mode. In the pass-through mode, the measurement signal from the measuring device is transmitted to the transducer without signal processing. In the envelope mode, envelope components are extracted from the measurement signal in response to a suitable time constant (cut-off frequency) and then transmitted to the transducer. In the FIR mode, signal components within a specified range are extracted from the measurement signal like a band-pass filter and then transmitted to the transducer. In the Wavelet transformation mode, based on the Wavelet transformation, frequency components corresponding to Wavelet coefficients equal to or less than a specified threshold are removed from the measurement signal transmitted to the transducer. In the cepstrum mode, based on the cepstrum analysis, power components equal to or less than a specified threshold are removed from the measurement signal transmitted to the transducer.
The transducer of the second prior art is configured to operate in any mode of a projection wave form mode, an FFT (fast Fourier transform) mode, an FFT+Wavelet transformation mode, a probability density function mode and an effective value mode to extract a feature (i.e., feature data) from the output of the filter. In the projection wave form mode, the feature is extracted by integrating signal amplitude from the filter based on a window function. In the FFT mode, the feature is extracted by calculating Fourier coefficients based on the fast Fourier transform. In the FFT+Wavelet transformation mode, the feature is extracted by wavelet-transforming a frequency distribution pattern obtained from the fast Fourier transform. In the probability density function mode, the feature is extracted by working out a probability density function. In the effective value mode, the feature is extracted by working out an effective value.
In a learning mode or the like, the signal discrimination apparatus calculates each accuracy of all combinations of filter and transducer modes and ranks each combination according to accuracy order. Prior to an inspecting mode, the apparatus selects one combination corresponding to the highest accuracy from some combinations of filter and transducer modes selected by a user, and sets the signal processor to the selected combination mode.
Japanese Patent Application Publication Number 2006-072659 published Mar. 16, 2006 discloses signal discrimination method and apparatus (hereinafter referred to as a “third prior art”). The apparatus has a measuring device, a competitive learning neural network, a display device and so on in the same way as the first prior art, and further has a transducer located between the measuring device and the network. The transducer is configured to extract a feature (i.e., feature data) from at least one extraction range. For example, the transducer extracts a feature from the components in an extraction range of the measurement signal obtained through the measuring device, or extracts a feature from the measurement signal to further extract a feature in an extraction range. The apparatus changes the upper and lower limits of each specified range for determining the at least one extraction range, and then calculates accuracy of category classification of the feature data every specified range. The apparatus then sets one or more specified ranges corresponding to the highest accuracy to the at least one extraction range of the transducer.
In the second and third prior arts, the accuracy of category judgment of the feature data can be improved by adjusting a combination of modes of the signal processor or adjusting at least one extraction range for the transducer. However, if an unsuitable learning data set is used, the accuracy of category judgment of the feature data cannot be improved.